Implemented training script with hydra

4 files changed, 446 insertions, 444 deletions

diff --git a/text_recognizer/networks/__init__.py b/text_recognizer/networks/__init__.py
index a9117f8..d1ebf1a 100644
--- a/text_recognizer/networks/__init__.py
+++ b/text_recognizer/networks/__init__.py
@@ -1,4 +1,5 @@
 """Network modules"""
 from .encoders import EfficientNet
 from .vqvae import VQVAE
-from .cnn_transformer import CNNTransformer
+
+# from .cnn_transformer import CNNTransformer
diff --git a/text_recognizer/networks/cnn_transformer.py b/text_recognizer/networks/cnn_transformer.py
index d42c29d..80798e1 100644
--- a/text_recognizer/networks/cnn_transformer.py
+++ b/text_recognizer/networks/cnn_transformer.py
@@ -1,182 +1,182 @@
-"""A Transformer with a cnn backbone.
-
-The network encodes a image with a convolutional backbone to a latent representation,
-i.e. feature maps. A 2d positional encoding is applied to the feature maps for
-spatial information. The resulting feature are then set to a transformer decoder
-together with the target tokens.
-
-TODO: Local attention for lower layer in attention.
-
-"""
-import importlib
-import math
-from typing import Dict, Optional, Union, Sequence, Type
-
-from einops import rearrange
-from omegaconf import DictConfig, OmegaConf
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.data.emnist import NUM_SPECIAL_TOKENS
-from text_recognizer.networks.transformer import (
-    Decoder,
-    DecoderLayer,
-    PositionalEncoding,
-    PositionalEncoding2D,
-    target_padding_mask,
-)
-
-NUM_WORD_PIECES = 1000
-
-
-class CNNTransformer(nn.Module):
-    def __init__(
-        self,
-        input_dim: Sequence[int],
-        output_dims: Sequence[int],
-        encoder: Union[DictConfig, Dict],
-        vocab_size: Optional[int] = None,
-        num_decoder_layers: int = 4,
-        hidden_dim: int = 256,
-        num_heads: int = 4,
-        expansion_dim: int = 1024,
-        dropout_rate: float = 0.1,
-        transformer_activation: str = "glu",
-        *args,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-        self.vocab_size = (
-            NUM_WORD_PIECES + NUM_SPECIAL_TOKENS if vocab_size is None else vocab_size
-        )
-        self.pad_index = 3  # TODO: fix me
-        self.hidden_dim = hidden_dim
-        self.max_output_length = output_dims[0]
-
-        # Image backbone
-        self.encoder = self._configure_encoder(encoder)
-        self.encoder_proj = nn.Conv2d(256, hidden_dim, kernel_size=1)
-        self.feature_map_encoding = PositionalEncoding2D(
-            hidden_dim=hidden_dim, max_h=input_dim[1], max_w=input_dim[2]
-        )
-
-        # Target token embedding
-        self.trg_embedding = nn.Embedding(self.vocab_size, hidden_dim)
-        self.trg_position_encoding = PositionalEncoding(
-            hidden_dim, dropout_rate, max_len=output_dims[0]
-        )
-
-        # Transformer decoder
-        self.decoder = Decoder(
-            decoder_layer=DecoderLayer(
-                hidden_dim=hidden_dim,
-                num_heads=num_heads,
-                expansion_dim=expansion_dim,
-                dropout_rate=dropout_rate,
-                activation=transformer_activation,
-            ),
-            num_layers=num_decoder_layers,
-            norm=nn.LayerNorm(hidden_dim),
-        )
-
-        # Classification head
-        self.head = nn.Linear(hidden_dim, self.vocab_size)
-
-        # Initialize weights
-        self._init_weights()
-
-    def _init_weights(self) -> None:
-        """Initialize network weights."""
-        self.trg_embedding.weight.data.uniform_(-0.1, 0.1)
-        self.head.bias.data.zero_()
-        self.head.weight.data.uniform_(-0.1, 0.1)
-
-        nn.init.kaiming_normal_(
-            self.encoder_proj.weight.data,
-            a=0,
-            mode="fan_out",
-            nonlinearity="relu",
-        )
-        if self.encoder_proj.bias is not None:
-            _, fan_out = nn.init._calculate_fan_in_and_fan_out(
-                self.encoder_proj.weight.data
-            )
-            bound = 1 / math.sqrt(fan_out)
-            nn.init.normal_(self.encoder_proj.bias, -bound, bound)
-
-    @staticmethod
-    def _configure_encoder(encoder: Union[DictConfig, Dict]) -> Type[nn.Module]:
-        encoder = OmegaConf.create(encoder)
-        args = encoder.args or {}
-        network_module = importlib.import_module("text_recognizer.networks")
-        encoder_class = getattr(network_module, encoder.type)
-        return encoder_class(**args)
-
-    def encode(self, image: Tensor) -> Tensor:
-        """Extracts image features with backbone.
-
-        Args:
-            image (Tensor): Image(s) of handwritten text.
-
-        Retuns:
-            Tensor: Image features.
-
-        Shapes:
-            - image: :math: `(B, C, H, W)`
-            - latent: :math: `(B, T, C)`
-
-        """
-        # Extract image features.
-        image_features = self.encoder(image)
-        image_features = self.encoder_proj(image_features)
-
-        # Add 2d encoding to the feature maps.
-        image_features = self.feature_map_encoding(image_features)
-
-        # Collapse features maps height and width.
-        image_features = rearrange(image_features, "b c h w -> b (h w) c")
-        return image_features
-
-    def decode(self, memory: Tensor, trg: Tensor) -> Tensor:
-        """Decodes image features with transformer decoder."""
-        trg_mask = target_padding_mask(trg=trg, pad_index=self.pad_index)
-        trg = self.trg_embedding(trg) * math.sqrt(self.hidden_dim)
-        trg = rearrange(trg, "b t d -> t b d")
-        trg = self.trg_position_encoding(trg)
-        trg = rearrange(trg, "t b d -> b t d")
-        out = self.decoder(trg=trg, memory=memory, trg_mask=trg_mask, memory_mask=None)
-        logits = self.head(out)
-        return logits
-
-    def forward(self, image: Tensor, trg: Tensor) -> Tensor:
-        image_features = self.encode(image)
-        output = self.decode(image_features, trg)
-        output = rearrange(output, "b t c -> b c t")
-        return output
-
-    def predict(self, image: Tensor) -> Tensor:
-        """Transcribes text in image(s)."""
-        bsz = image.shape[0]
-        image_features = self.encode(image)
-
-        output_tokens = (
-            (torch.ones((bsz, self.max_output_length)) * self.pad_index)
-            .type_as(image)
-            .long()
-        )
-        output_tokens[:, 0] = self.start_index
-        for i in range(1, self.max_output_length):
-            trg = output_tokens[:, :i]
-            output = self.decode(image_features, trg)
-            output = torch.argmax(output, dim=-1)
-            output_tokens[:, i] = output[-1:]
-
-        # Set all tokens after end token to be padding.
-        for i in range(1, self.max_output_length):
-            indices = output_tokens[:, i - 1] == self.end_index | (
-                output_tokens[:, i - 1] == self.pad_index
-            )
-            output_tokens[indices, i] = self.pad_index
-
-        return output_tokens
+# """A Transformer with a cnn backbone.
+#
+# The network encodes a image with a convolutional backbone to a latent representation,
+# i.e. feature maps. A 2d positional encoding is applied to the feature maps for
+# spatial information. The resulting feature are then set to a transformer decoder
+# together with the target tokens.
+#
+# TODO: Local attention for lower layer in attention.
+#
+# """
+# import importlib
+# import math
+# from typing import Dict, Optional, Union, Sequence, Type
+#
+# from einops import rearrange
+# from omegaconf import DictConfig, OmegaConf
+# import torch
+# from torch import nn
+# from torch import Tensor
+#
+# from text_recognizer.data.emnist import NUM_SPECIAL_TOKENS
+# from text_recognizer.networks.transformer import (
+#     Decoder,
+#     DecoderLayer,
+#     PositionalEncoding,
+#     PositionalEncoding2D,
+#     target_padding_mask,
+# )
+#
+# NUM_WORD_PIECES = 1000
+#
+#
+# class CNNTransformer(nn.Module):
+#     def __init__(
+#         self,
+#         input_dim: Sequence[int],
+#         output_dims: Sequence[int],
+#         encoder: Union[DictConfig, Dict],
+#         vocab_size: Optional[int] = None,
+#         num_decoder_layers: int = 4,
+#         hidden_dim: int = 256,
+#         num_heads: int = 4,
+#         expansion_dim: int = 1024,
+#         dropout_rate: float = 0.1,
+#         transformer_activation: str = "glu",
+#         *args,
+#         **kwargs,
+#     ) -> None:
+#         super().__init__()
+#         self.vocab_size = (
+#             NUM_WORD_PIECES + NUM_SPECIAL_TOKENS if vocab_size is None else vocab_size
+#         )
+#         self.pad_index = 3  # TODO: fix me
+#         self.hidden_dim = hidden_dim
+#         self.max_output_length = output_dims[0]
+#
+#         # Image backbone
+#         self.encoder = self._configure_encoder(encoder)
+#         self.encoder_proj = nn.Conv2d(256, hidden_dim, kernel_size=1)
+#         self.feature_map_encoding = PositionalEncoding2D(
+#             hidden_dim=hidden_dim, max_h=input_dim[1], max_w=input_dim[2]
+#         )
+#
+#         # Target token embedding
+#         self.trg_embedding = nn.Embedding(self.vocab_size, hidden_dim)
+#         self.trg_position_encoding = PositionalEncoding(
+#             hidden_dim, dropout_rate, max_len=output_dims[0]
+#         )
+#
+#         # Transformer decoder
+#         self.decoder = Decoder(
+#             decoder_layer=DecoderLayer(
+#                 hidden_dim=hidden_dim,
+#                 num_heads=num_heads,
+#                 expansion_dim=expansion_dim,
+#                 dropout_rate=dropout_rate,
+#                 activation=transformer_activation,
+#             ),
+#             num_layers=num_decoder_layers,
+#             norm=nn.LayerNorm(hidden_dim),
+#         )
+#
+#         # Classification head
+#         self.head = nn.Linear(hidden_dim, self.vocab_size)
+#
+#         # Initialize weights
+#         self._init_weights()
+#
+#     def _init_weights(self) -> None:
+#         """Initialize network weights."""
+#         self.trg_embedding.weight.data.uniform_(-0.1, 0.1)
+#         self.head.bias.data.zero_()
+#         self.head.weight.data.uniform_(-0.1, 0.1)
+#
+#         nn.init.kaiming_normal_(
+#             self.encoder_proj.weight.data,
+#             a=0,
+#             mode="fan_out",
+#             nonlinearity="relu",
+#         )
+#         if self.encoder_proj.bias is not None:
+#             _, fan_out = nn.init._calculate_fan_in_and_fan_out(
+#                 self.encoder_proj.weight.data
+#             )
+#             bound = 1 / math.sqrt(fan_out)
+#             nn.init.normal_(self.encoder_proj.bias, -bound, bound)
+#
+#     @staticmethod
+#     def _configure_encoder(encoder: Union[DictConfig, Dict]) -> Type[nn.Module]:
+#         encoder = OmegaConf.create(encoder)
+#         args = encoder.args or {}
+#         network_module = importlib.import_module("text_recognizer.networks")
+#         encoder_class = getattr(network_module, encoder.type)
+#         return encoder_class(**args)
+#
+#     def encode(self, image: Tensor) -> Tensor:
+#         """Extracts image features with backbone.
+#
+#         Args:
+#             image (Tensor): Image(s) of handwritten text.
+#
+#         Retuns:
+#             Tensor: Image features.
+#
+#         Shapes:
+#             - image: :math: `(B, C, H, W)`
+#             - latent: :math: `(B, T, C)`
+#
+#         """
+#         # Extract image features.
+#         image_features = self.encoder(image)
+#         image_features = self.encoder_proj(image_features)
+#
+#         # Add 2d encoding to the feature maps.
+#         image_features = self.feature_map_encoding(image_features)
+#
+#         # Collapse features maps height and width.
+#         image_features = rearrange(image_features, "b c h w -> b (h w) c")
+#         return image_features
+#
+#     def decode(self, memory: Tensor, trg: Tensor) -> Tensor:
+#         """Decodes image features with transformer decoder."""
+#         trg_mask = target_padding_mask(trg=trg, pad_index=self.pad_index)
+#         trg = self.trg_embedding(trg) * math.sqrt(self.hidden_dim)
+#         trg = rearrange(trg, "b t d -> t b d")
+#         trg = self.trg_position_encoding(trg)
+#         trg = rearrange(trg, "t b d -> b t d")
+#         out = self.decoder(trg=trg, memory=memory, trg_mask=trg_mask, memory_mask=None)
+#         logits = self.head(out)
+#         return logits
+#
+#     def forward(self, image: Tensor, trg: Tensor) -> Tensor:
+#         image_features = self.encode(image)
+#         output = self.decode(image_features, trg)
+#         output = rearrange(output, "b t c -> b c t")
+#         return output
+#
+#     def predict(self, image: Tensor) -> Tensor:
+#         """Transcribes text in image(s)."""
+#         bsz = image.shape[0]
+#         image_features = self.encode(image)
+#
+#         output_tokens = (
+#             (torch.ones((bsz, self.max_output_length)) * self.pad_index)
+#             .type_as(image)
+#             .long()
+#         )
+#         output_tokens[:, 0] = self.start_index
+#         for i in range(1, self.max_output_length):
+#             trg = output_tokens[:, :i]
+#             output = self.decode(image_features, trg)
+#             output = torch.argmax(output, dim=-1)
+#             output_tokens[:, i] = output[-1:]
+#
+#         # Set all tokens after end token to be padding.
+#         for i in range(1, self.max_output_length):
+#             indices = output_tokens[:, i - 1] == self.end_index | (
+#                 output_tokens[:, i - 1] == self.pad_index
+#             )
+#             output_tokens[indices, i] = self.pad_index
+#
+#         return output_tokens
diff --git a/text_recognizer/networks/transformer/__init__.py b/text_recognizer/networks/transformer/__init__.py
index 627fa7b..4ff48f7 100644
--- a/text_recognizer/networks/transformer/__init__.py
+++ b/text_recognizer/networks/transformer/__init__.py
@@ -4,4 +4,5 @@ from .positional_encoding import (
     PositionalEncoding2D,
     target_padding_mask,
 )
-from .transformer import Decoder, DecoderLayer, Encoder, EncoderLayer, Transformer
+
+# from .transformer import Decoder, DecoderLayer, Encoder, EncoderLayer, Transformer
diff --git a/text_recognizer/networks/transformer/transformer.py b/text_recognizer/networks/transformer/transformer.py
index 5ac2787..d49c85a 100644
--- a/text_recognizer/networks/transformer/transformer.py
+++ b/text_recognizer/networks/transformer/transformer.py
@@ -1,260 +1,260 @@
-"""Transfomer module."""
-import copy
-from typing import Dict, Optional, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-
-from text_recognizer.networks.transformer.attention import MultiHeadAttention
-from text_recognizer.networks.util import activation_function
-
-
-class GEGLU(nn.Module):
-    """GLU activation for improving feedforward activations."""
-
-    def __init__(self, dim_in: int, dim_out: int) -> None:
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward propagation."""
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
-
-
-class _IntraLayerConnection(nn.Module):
-    """Preforms the residual connection inside the transfomer blocks and applies layernorm."""
-
-    def __init__(self, dropout_rate: float, hidden_dim: int) -> None:
-        super().__init__()
-        self.norm = nn.LayerNorm(normalized_shape=hidden_dim)
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward(self, src: Tensor, residual: Tensor) -> Tensor:
-        return self.norm(self.dropout(src) + residual)
-
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-
-        in_projection = (
-            nn.Sequential(
-                nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
-            )
-            if activation != "glu"
-            else GEGLU(hidden_dim, expansion_dim)
-        )
-
-        self.layer = nn.Sequential(
-            in_projection,
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.layer(x)
-
-
-class EncoderLayer(nn.Module):
-    """Transfomer encoding layer."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.mlp = FeedForward(hidden_dim, expansion_dim, dropout_rate, activation)
-        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
-
-    def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor:
-        """Forward pass through the encoder."""
-        # First block.
-        # Multi head attention.
-        out, _ = self.self_attention(src, src, src, mask)
-
-        # Add & norm.
-        out = self.block1(out, src)
-
-        # Second block.
-        # Apply 1D-convolution.
-        mlp_out = self.mlp(out)
-
-        # Add & norm.
-        out = self.block2(mlp_out, out)
-
-        return out
-
-
-class Encoder(nn.Module):
-    """Transfomer encoder module."""
-
-    def __init__(
-        self,
-        num_layers: int,
-        encoder_layer: Type[nn.Module],
-        norm: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.norm = norm
-
-    def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor:
-        """Forward pass through all encoder layers."""
-        for layer in self.layers:
-            src = layer(src, src_mask)
-
-        if self.norm is not None:
-            src = self.norm(src)
-
-        return src
-
-
-class DecoderLayer(nn.Module):
-    """Transfomer decoder layer."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float = 0.0,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.multihead_attention = MultiHeadAttention(
-            hidden_dim, num_heads, dropout_rate
-        )
-        self.mlp = FeedForward(hidden_dim, expansion_dim, dropout_rate, activation)
-        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
-
-    def forward(
-        self,
-        trg: Tensor,
-        memory: Tensor,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass of the layer."""
-        out, _ = self.self_attention(trg, trg, trg, trg_mask)
-        trg = self.block1(out, trg)
-
-        out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
-        trg = self.block2(out, trg)
-
-        out = self.mlp(trg)
-        out = self.block3(out, trg)
-
-        return out
-
-
-class Decoder(nn.Module):
-    """Transfomer decoder module."""
-
-    def __init__(
-        self,
-        decoder_layer: Type[nn.Module],
-        num_layers: int,
-        norm: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__()
-        self.layers = _get_clones(decoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        trg: Tensor,
-        memory: Tensor,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass through the decoder."""
-        for layer in self.layers:
-            trg = layer(trg, memory, trg_mask, memory_mask)
-
-        if self.norm is not None:
-            trg = self.norm(trg)
-
-        return trg
-
-
-class Transformer(nn.Module):
-    """Transformer network."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-
-        # Configure encoder.
-        encoder_norm = nn.LayerNorm(hidden_dim)
-        encoder_layer = EncoderLayer(
-            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
-        )
-        self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm)
-
-        # Configure decoder.
-        decoder_norm = nn.LayerNorm(hidden_dim)
-        decoder_layer = DecoderLayer(
-            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
-        )
-        self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self) -> None:
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(
-        self,
-        src: Tensor,
-        trg: Tensor,
-        src_mask: Optional[Tensor] = None,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass through the transformer."""
-        if src.shape[0] != trg.shape[0]:
-            print(trg.shape)
-            raise RuntimeError("The batch size of the src and trg must be the same.")
-        if src.shape[2] != trg.shape[2]:
-            raise RuntimeError(
-                "The number of features for the src and trg must be the same."
-            )
-
-        memory = self.encoder(src, src_mask)
-        output = self.decoder(trg, memory, trg_mask, memory_mask)
-        return output
+# """Transfomer module."""
+# import copy
+# from typing import Dict, Optional, Type, Union
+#
+# import numpy as np
+# import torch
+# from torch import nn
+# from torch import Tensor
+# import torch.nn.functional as F
+#
+# from text_recognizer.networks.transformer.attention import MultiHeadAttention
+# from text_recognizer.networks.util import activation_function
+#
+#
+# class GEGLU(nn.Module):
+#     """GLU activation for improving feedforward activations."""
+#
+#     def __init__(self, dim_in: int, dim_out: int) -> None:
+#         super().__init__()
+#         self.proj = nn.Linear(dim_in, dim_out * 2)
+#
+#     def forward(self, x: Tensor) -> Tensor:
+#         """Forward propagation."""
+#         x, gate = self.proj(x).chunk(2, dim=-1)
+#         return x * F.gelu(gate)
+#
+#
+# def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
+#     return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
+#
+#
+# class _IntraLayerConnection(nn.Module):
+#     """Preforms the residual connection inside the transfomer blocks and applies layernorm."""
+#
+#     def __init__(self, dropout_rate: float, hidden_dim: int) -> None:
+#         super().__init__()
+#         self.norm = nn.LayerNorm(normalized_shape=hidden_dim)
+#         self.dropout = nn.Dropout(p=dropout_rate)
+#
+#     def forward(self, src: Tensor, residual: Tensor) -> Tensor:
+#         return self.norm(self.dropout(src) + residual)
+#
+#
+# class FeedForward(nn.Module):
+#     def __init__(
+#         self,
+#         hidden_dim: int,
+#         expansion_dim: int,
+#         dropout_rate: float,
+#         activation: str = "relu",
+#     ) -> None:
+#         super().__init__()
+#
+#         in_projection = (
+#             nn.Sequential(
+#                 nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
+#             )
+#             if activation != "glu"
+#             else GEGLU(hidden_dim, expansion_dim)
+#         )
+#
+#         self.layer = nn.Sequential(
+#             in_projection,
+#             nn.Dropout(p=dropout_rate),
+#             nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
+#         )
+#
+#     def forward(self, x: Tensor) -> Tensor:
+#         return self.layer(x)
+#
+#
+# class EncoderLayer(nn.Module):
+#     """Transfomer encoding layer."""
+#
+#     def __init__(
+#         self,
+#         hidden_dim: int,
+#         num_heads: int,
+#         expansion_dim: int,
+#         dropout_rate: float,
+#         activation: str = "relu",
+#     ) -> None:
+#         super().__init__()
+#         self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
+#         self.mlp = FeedForward(hidden_dim, expansion_dim, dropout_rate, activation)
+#         self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
+#         self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
+#
+#     def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor:
+#         """Forward pass through the encoder."""
+#         # First block.
+#         # Multi head attention.
+#         out, _ = self.self_attention(src, src, src, mask)
+#
+#         # Add & norm.
+#         out = self.block1(out, src)
+#
+#         # Second block.
+#         # Apply 1D-convolution.
+#         mlp_out = self.mlp(out)
+#
+#         # Add & norm.
+#         out = self.block2(mlp_out, out)
+#
+#         return out
+#
+#
+# class Encoder(nn.Module):
+#     """Transfomer encoder module."""
+#
+#     def __init__(
+#         self,
+#         num_layers: int,
+#         encoder_layer: Type[nn.Module],
+#         norm: Optional[Type[nn.Module]] = None,
+#     ) -> None:
+#         super().__init__()
+#         self.layers = _get_clones(encoder_layer, num_layers)
+#         self.norm = norm
+#
+#     def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor:
+#         """Forward pass through all encoder layers."""
+#         for layer in self.layers:
+#             src = layer(src, src_mask)
+#
+#         if self.norm is not None:
+#             src = self.norm(src)
+#
+#         return src
+#
+#
+# class DecoderLayer(nn.Module):
+#     """Transfomer decoder layer."""
+#
+#     def __init__(
+#         self,
+#         hidden_dim: int,
+#         num_heads: int,
+#         expansion_dim: int,
+#         dropout_rate: float = 0.0,
+#         activation: str = "relu",
+#     ) -> None:
+#         super().__init__()
+#         self.hidden_dim = hidden_dim
+#         self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
+#         self.multihead_attention = MultiHeadAttention(
+#             hidden_dim, num_heads, dropout_rate
+#         )
+#         self.mlp = FeedForward(hidden_dim, expansion_dim, dropout_rate, activation)
+#         self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
+#         self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
+#         self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
+#
+#     def forward(
+#         self,
+#         trg: Tensor,
+#         memory: Tensor,
+#         trg_mask: Optional[Tensor] = None,
+#         memory_mask: Optional[Tensor] = None,
+#     ) -> Tensor:
+#         """Forward pass of the layer."""
+#         out, _ = self.self_attention(trg, trg, trg, trg_mask)
+#         trg = self.block1(out, trg)
+#
+#         out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
+#         trg = self.block2(out, trg)
+#
+#         out = self.mlp(trg)
+#         out = self.block3(out, trg)
+#
+#         return out
+#
+#
+# class Decoder(nn.Module):
+#     """Transfomer decoder module."""
+#
+#     def __init__(
+#         self,
+#         decoder_layer: Type[nn.Module],
+#         num_layers: int,
+#         norm: Optional[Type[nn.Module]] = None,
+#     ) -> None:
+#         super().__init__()
+#         self.layers = _get_clones(decoder_layer, num_layers)
+#         self.num_layers = num_layers
+#         self.norm = norm
+#
+#     def forward(
+#         self,
+#         trg: Tensor,
+#         memory: Tensor,
+#         trg_mask: Optional[Tensor] = None,
+#         memory_mask: Optional[Tensor] = None,
+#     ) -> Tensor:
+#         """Forward pass through the decoder."""
+#         for layer in self.layers:
+#             trg = layer(trg, memory, trg_mask, memory_mask)
+#
+#         if self.norm is not None:
+#             trg = self.norm(trg)
+#
+#         return trg
+#
+#
+# class Transformer(nn.Module):
+#     """Transformer network."""
+#
+#     def __init__(
+#         self,
+#         num_encoder_layers: int,
+#         num_decoder_layers: int,
+#         hidden_dim: int,
+#         num_heads: int,
+#         expansion_dim: int,
+#         dropout_rate: float,
+#         activation: str = "relu",
+#     ) -> None:
+#         super().__init__()
+#
+#         # Configure encoder.
+#         encoder_norm = nn.LayerNorm(hidden_dim)
+#         encoder_layer = EncoderLayer(
+#             hidden_dim, num_heads, expansion_dim, dropout_rate, activation
+#         )
+#         self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm)
+#
+#         # Configure decoder.
+#         decoder_norm = nn.LayerNorm(hidden_dim)
+#         decoder_layer = DecoderLayer(
+#             hidden_dim, num_heads, expansion_dim, dropout_rate, activation
+#         )
+#         self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm)
+#
+#         self._reset_parameters()
+#
+#     def _reset_parameters(self) -> None:
+#         for p in self.parameters():
+#             if p.dim() > 1:
+#                 nn.init.xavier_uniform_(p)
+#
+#     def forward(
+#         self,
+#         src: Tensor,
+#         trg: Tensor,
+#         src_mask: Optional[Tensor] = None,
+#         trg_mask: Optional[Tensor] = None,
+#         memory_mask: Optional[Tensor] = None,
+#     ) -> Tensor:
+#         """Forward pass through the transformer."""
+#         if src.shape[0] != trg.shape[0]:
+#             print(trg.shape)
+#             raise RuntimeError("The batch size of the src and trg must be the same.")
+#         if src.shape[2] != trg.shape[2]:
+#             raise RuntimeError(
+#                 "The number of features for the src and trg must be the same."
+#             )
+#
+#         memory = self.encoder(src, src_mask)
+#         output = self.decoder(trg, memory, trg_mask, memory_mask)
+#         return output